Understanding Sprinting Motion Skills using Unsupervised Learning for Stepwise Skill Improvements of Running Motion

被引：2

作者：

Seo, Chanjin ^{[1
]}

Sabanai, Masato ^{[1
]}

Ogata, Hiroyuki ^{[2
]}

Ohya, Jun ^{[1
]}

机构：

[1] Waseda Univ, Dept Modern Mech Engn, Shinjuku Ku, 3-4-1 Ookubo, Tokyo, Japan

[2] Seikei Univ, Fac Sci & Technol, 3-3-1 Kichij Kitamachi, Musashino, Tokyo, Japan

来源：

ICPRAM: PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION APPLICATIONS AND METHODS | 2019年

关键词：

Running Motion; Unsupervised Learning; Coaching System; Stepwise Skill Improvement;

D O I：

10.5220/0007358804670475

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

To improve running performances, each runner's skill, such as characteristics and habits, needs to be known, and feedback on the performance should be outputted according to the runner's skill level. In this paper, we propose a new coaching system for detecting the skill of a runner and a method of giving feedback using a sprint motion dataset. Our proposed method calculates an extracted feature to detect the skill using an autoencoder whose middle layer is an LSTM layer; we analyse the feature using hierarchical clustering, and we analyse the human joints that affect the skill. As a result of experiments, five clusters are obtained using hierarchical clustering. This paper clarifies how to detect the skill and to output feedback to achieve a level of performance one step higher than the current level.

引用

页码：467 / 475

页数：9